Distributed Denial of Service (DDoS) threats in Smart Grid are very challenging and considered one of the most destructive cyber-attacks. They are harmfully affecting the power sector and causing substantial financial loss. Classical Reinforcement Learning (RL) models are proposed as a promising solution to the DDoS problem as they learn optimal mitigation policies under different attack scenarios. However, as the RL environment is generally dynamic, which leads to more computation capabilities, classical RL efficiency can be limited as a result of serial processing using classical computing. In this paper, a Quantum Entropy Q-Learning (QEQ) is proposed to fight DDoS in Smart Grid. The proposed framework is compared to a classical Q-Learning (QL) model with and without the Entropy method. The convergence speed and the total rewards performed with the same conditions for QL and are Entropy Q-Learning (EQ) analyzed to show the QEQ performance. Moreover, the accuracy, precision, recall, and F1 score are evaluated to prove the effectiveness of our QEQ in fighting DDoS attacks. Using the Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS 2019) dataset, a thorough systematic simulation using MATLAB, Python, the open-source Qiskit software, and the Harrow-Hassidim-Lloyd (HHL) quantum algorithm is carried out. This proposed QEQ is better performing the DDoS detection as it is faster enough and more adaptable to dynamic environment changing and able to improve the agent’s decision-making in a changing state action space. Finally, conclusions with some open issues of the Quantum Q-Learning are presented.

Thermal therapy is a promising treatment for many patients particularly those with surgery intolerance. It has been used since the early age of humanity. Nowadays, the thermal therapy has proved its efficacy, especially in Oncology. In fact, the tumor ablation using a different modality of heating or cooling presents a therapeutic method to ablate the extra mass and eradicate the malignant it. The administration of the energy is performed in various ways using an energy source. Following the level of the used temperature and the nature of the used heating or cooling source, many modalities have been developed. Hyperthermia uses the lowest temperature among them. Other modalities use an elevated temperature to kill and destroy the tumors. These ablative therapies are Radio Frequency Ablation (RFA), Micro Wave Ablation (MWA), High Intensity Focused Ultrasound (HIFU), LASER Ablation (LA), and Cryoablation(CA). With the purpose of doing a general state of the art of the thermal therapies applied in Oncology and enlightening their advantages and limitations, we reviewed many recent and pertinent published works in different databases. We also investigated the cell destruction mechanism due to temperature. We exposed the recent advances and the limitations of these modalities. However, to improve the quality of the treatment and increase the rate of survival, the research is still investigating these modalities. We discussed the future challenges and perspectives of the thermal therapies. Propositions to overpass the limitations of some techniques were introduced.

To meet the target of carbon neutrality by the year 2050 and decrease the dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, and Electric Vehicles (EVs) have to be massively deployed. Nevertheless, the integration of a large amount of wind power, with an intermittent nature, into the grid and the variability of the load on the demand side require an efficient and reliable energy management system (EMS) for operation, scheduling, maintenance and energy trading in the modern power system. This article proposes a new Energy Management Protocol (EMP) based on the combination of Machine Learning (ML) and Game-Theoretic (GT) algorithms to manage the operation of the charging/discharging of EVs from an energy storage system (ESS) via EV supply equipment (EVSE) when the main source of energy is wind power. The ESS can be linked to the grid to overcome downtimes of wind power production. Case study results of wind power forecasting using an ML algorithm and 10 min wind measurements, combined with a GT optimization model, showed good performance in the forecasting and management of power dispatching between EVs to ensure the efficient and accurate operation of the power system.

The basic need common to all living beings is water. Less than 1% of the water on earth is fresh water and water use is increasing daily. Agricultural practices alone require huge amounts of water. The drip technique improved the efficiency of water use in irrigation and initiated the introduction and development of fertigation, the integrated distribution of water and fertilizer. The past few decades have seen extensive research being carried out in the area of development and evaluation of different technologies available to estimate/measure soil moisture to aid in various applications and to facilitate the use of drip irrigation for users and farmers. In this technology, plant moisture and temperature are accurately monitored and controlled in real time over roots in the form of droplets, by developing smart monitoring system to save water and avoid water waste using drip irrigation technology. Water is delivered to the roots drop by drop, which saves water as well as prevents plants from being flooded and decaying due to excess water released by irrigation methods such as flood irrigation, border irrigation, furrow irrigation, and control basin irrigation. Drip irrigation with an embedded intelligent monitoring system is one of the most valuable techniques used to save water and farmers’ time and energy. In this paper, we design an embedded monitoring system based in the integrated 65 nm CMOS technology in agricultural practices which would facilitate agriculture and enable farmers to monitor crops. Hence, to demonstrate the feasibility, a prototype was constructed and simulated with modelsim and validated with nclaunch the both tools from Cadence, as well as implementation on the FPGA board, was be performed.

With the increasing number of new attacks, virtualized and distributed networks require greater attention and investment in cybersecurity. Organizations must rely on effective Intrusion Detection Systems (IDS) to detect both known and novel attacks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques have been widely used for intrusion detection. Several studies have reviewed ML and DL-based detection models, but they often overlook the specific networks targeted by these models. It is crucial to understand not only which methods are effective but also the contexts in which they are effective. This study aims to fill this gap by reviewing and classifying recent contributions based on their target networks. It focuses on three key network types: Cloud Computing (CC), Internet of Things (IoT), and Software-Defined Networks (SDN). Our study emphasizes the importance of thoroughly understanding the strengths and vulnerabilities of a given network, which is an important step towards developing effective ML- and DL-based intrusion detection approaches. We first provide an overview of related works and our research steps, followed by a presentation of ML and DL techniques, and commonly used datasets in this field. Next, a detailed presentation of the current research on IDS based on ML and DL techniques by network categories is provided. The strengths and limitations of ML and DL algorithms, which are frequently used for intrusion detection, are highlighted. Finally, the challenges are discussed and future research directions are proposed.

Quantum Hardware Devices (QHDs) have marked a revolutionary leap in computational power, harnessing the principles of superposition, entanglement, and quantum interference to surpass classical processing capabilities. This review focuses on the core building blocks of quantum computing qubits, quantum gates, registers, and chipsets, while emphasizing the pivotal role of advanced architectures, such as Field-Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs), in optimizing quantum information processing. Despite their transformative potential, QHDs face critical hurdles, such as decoherence, Quantum Error Correction (QEC), scalability, and seamless integration with classical computing systems. Overcoming these challenges requires breakthroughs in materials science, cryogenic engineering, and quantum error mitigation. In addition, the development of high-fidelity quantum processors is essential for building robust Quantum Computing Systems (QCS) capable of solving problems beyond the reach of classical computation. This paper presents a comprehensive analysis of Emerging Quantum Technologies (EQT), examining their profound impact on cryptography, Artificial Intelligence (AI), Quantum Simulation (QS), and Secure Communication (SC). By reviewing state-of-the-art research and engineering innovations, we outline the roadmap for transitioning from experimental prototypes to large-scale, fault-tolerant quantum systems, paving the way for the next era of computing.